Deriving an accurate indoor location of a wireless device has been an increasingly important topic of mobile wireless devices. Despite research and development efforts, enabling and providing accurate indoor location capability remains a challenging topic. Limitations of the current indoor location systems may be in regard to their performance inadequacy or due to requirements for substantial infrastructure to desired indoor location capabilities.
WiFi (i.e., Institute of Electrical and Electronics Engineers' (IEEE) 802.11 standards) based indoor location technologies particularly attracted a lot of attention given wide deployment of wireless local area network (WLAN) infrastructure. For example, there are current methods for indoor location determination that uses the WLAN infrastructure. One method is based on propagation models, using estimated degradation of signal strength over distance in space from a known location of access point (AP) and transmit power e.g., Sky Hook™). Another method relies on storing pre-recorded calibration WiFi measurement data (i.e., WiFi fingerprinting) in order to generate a radio frequency (RF) map of a building, such as Ekahau™ and Qubulus™. Another method utilizes a measurement of WiFi radio wave time of flight to measure distance.
“WiFi fingerprinting” has been shown that it may reach meter level accuracy as long as the pre-calibrated database has sufficiently dense calibration points (i.e., meter grid). The method derived from Sky Hook™ has a median error of approximately 11 m and mean error of approximately 25 m. The method using time of flight method may reach approximately about 3-5 meter accuracy, and may be constrained by indoor mufti-path environment. Existing implementations of the “WiFi fingerprinting,” such as the Ekahau™ and Qubulus™ may require initial training to correlate each location with corresponding WiFi received signal strength indicator (RSSI) fingerprint, which leads to substantial efforts to deploy such location systems, especially when the calibration points are dense. Also, the calibration points often need to be re-performed as deployment environment changes. The substantial deployment effort is one of the primary reasons why “WiFi finger printing” based location system has not been widely adopted. Accordingly, a solution may be implemented to provide precision accuracy for indoor location of the wireless device with minimal human deployment efforts.
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